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In this work, a new technique is proposed to study short-term exposure and adverse health effects. The presented approach uses hierarchical clusters with the following structure: each pair of two sequential days in 1 year is embedded in the year. We have 183 clusters per year with the embedded structure  . Time-series analysis is conducted using a conditional Poisson regression with the constructed clusters as a stratum. Unmeasured confounders such as seasonal and long-term trends are not modelled but are controlled by the structure of the clusters. The proposed technique is illustrated using four freely accessible databases, which contain complex simulated data. These data are available as the compressed R workspace files. Results based on the simulated data were very close to the truth based on the presented methodology. In addition, the case-crossover method with 1-month and 2-week window, and a conditional Poisson regression on 3-day clusters as a stratum, was also applied to the simulated data. Difficulties (high type I error rate) were observed for the case-crossover method in the presence of high concurvity in the simulated data. The proposed methods using various forms of a stratum were further applied to the Chicago mortality data. The considered methods have often different qualitative and quantitative estimations.

Use of two-point models in “Model choice in time-series studies of air pollution and mortality”